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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04415736
Other study ID # AISAH
Secondary ID
Status Completed
Phase
First received
Last updated
Start date October 1, 2015
Est. completion date May 2020

Study information

Verified date September 2020
Source Göteborg University
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected heart rate variability data in order to evaluate whether specific patterns could be found in patients developing delayed cerebral ischemia after subarachnoid hemorrhage.


Description:

Patients with aneurysmal subarachnoid haemorrhage (aSAH), develop delayed cerebral ischemia (DCI) in about 30% of the cases. DCI is associated with increased mortality, persistent neurological deficit as well as impaired quality of life. It would benefit both patients and society to decrease these neurological injuries. One clinical problem is that the diagnosis of cerebral ischemia in SAH patients often is delayed due to limitations in monitoring abilities. When detected, the neurological damage often turns out to be irreversible.

Several studies have used univariate and multivariate logistic regression analysis to identify risk factors for the development of delayed cerebral ischemia (DCI) in patients with subarachnoid haemorrhage. However, these studies are based on data collected about the patients (e.g. age, gender), and the precision of these statistical models has generally been found to be low. Recently, machine learning algorithms for the prediction of DCI using a combination of clinical and image data have also been evaluated .

However, prediction of DCI does not prevent DCI, to prevent DCI a monitoring system needs to be developed that can warn physicians of imminent risk of cerebral ischemia, making it possible to intervene and prevent cerebral ischemia.

Variations in the autonomous nervous system, such as changes in the balance between the sympathetic and the parasympathetic nervous systems, can be detected by using heart rate variability (HRV) monitoring. HRV has been reported as a predictor of poor outcome after traumatic brain injury and stroke, including subarachnoid haemorrhage. However, HRV monitoring for detection of incipient cerebral ischemia has not been thoroughly evaluated. In a study of patients with aSAH, we collected HRV continuously in up to 10 days after admission, but just a small part of the HRV data was analysed off-line. Fifteen of 55 patients developed DCI during the acute phase, and the off-line analyse of HRV showed that the low/high-frequency ratio increased more in patients that developed DCI (Ref). This led us to try to analyse all of the collected HRV with the help of machine learning processes, and a collaboration with computer/data scientists was initiated.

The overall aim of this study is to, with the help of computer/data scientist and machine learning processes, analyse collected HRV data in order to evaluate whether specific patterns could be found in patients developing DCI during the acute phase after subarachnoid hemorrhage.


Recruitment information / eligibility

Status Completed
Enrollment 64
Est. completion date May 2020
Est. primary completion date October 2016
Accepts healthy volunteers No
Gender All
Age group 16 Years to 100 Years
Eligibility Inclusion Criteria:

- Adult patients

- aneurysmal subarachnoid hemorrhage

- admitted to Neurointensive care unit at Sahlgrenska University Hospital, Gothenburg, Sweden

Exclusion Criteria:

- cardiac arrythmias

- use of pacemaker

Study Design


Related Conditions & MeSH terms


Intervention

Other:
No intervention, observational study


Locations

Country Name City State
Sweden Sahlgrenska University Hospital Gothenburg

Sponsors (1)

Lead Sponsor Collaborator
Göteborg University

Country where clinical trial is conducted

Sweden, 

Outcome

Type Measure Description Time frame Safety issue
Primary HRV data analysed by artificial intelligence for detection of DCI Variability of the time of R-R intervals of each heartbeat is measured. 2020
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